辽宁大学

The development of efficient photocatalysts for water purification remains challenged by rapid charge recombination and limited surface reactivity. Here, we report a rationally designed PHI/W₁₈O₄₉ heterojunction photocatalyst, integrating interfacial W–N bonds and oxygen vacancies to synergistically overcome these limitations. The optimized PHI/W₁₈O₄₉ heterojunction exhibits significantly enhanced visible-light absorption, efficient spatial charge separation, and improved surface reaction kinetics. Consequently, it demonstrates superior and versatile photocatalytic performance in degrading organic contaminants, along with stable recyclability and consistent performance in real water matrices, highlighting its potential for practical applications. Mechanistic studies combining DFT calculations and experimental characterization reveal that interfacial W–N bonds direct S-scheme electron transfer, while oxygen vacancies enhance reactant adsorption, jointly boosting charge separation and surface reactivity. This work establishes a design paradigm that couples’ defect-enhanced surface adsorption with directed interfacial electron flow, offering an effective strategy for developing robust photocatalysts for practical environmental remediation.

Temporal knowledge graph reasoning under extrapolation aims to predict future events, relying on a comprehensive perception of local evolutionary patterns and global critical histories. Recent studies attempt to jointly model long- and short-term dependencies from both local and global perspectives, achieving superior inference performance. However, these methods still face the following challenges: First, the capacity to identify global critical histories is insufficient due to the limitations of graph-based modeling and fragmented assessment strategies; Second, the semantic type constraints of query relation on compatible candidate entities are ignored. To address these issues, we propose MGCC, a novel neural-symbolic temporal knowledge graph reasoning framework with Multi-Granularity modeling and Compatibility Constraints. For the first challenge, we propose a dual-granularity fact encoder to jointly capture internal semantic relationships within facts, as well as the correlations between facts in terms of relational similarity and temporal proximity, through intra- and inter-fact sequence modeling. Meanwhile, we retain temporal subgraph modeling to capture local evolutionary patterns and adaptively integrate local and global information. For the second challenge, we design a compatibility-constrained candidate filtering mechanism that injects symbolic constraints into neural reasoning, extracting valid candidates from both explicit compatibility (historical interactions) and implicit compatibility (knowledge transfer) perspectives. Experiments on four benchmark datasets show that our proposed MGCC achieves state-of-the-art performance on the entity prediction task, improving MRR by 7.10%, 7.85%, 2.80%, and 8.29% over the best baselines, respectively.

The transition toward a hydrogen-based economy requires innovative storage materials that simultaneously enhance hydrogen performance, promote resource efficiency, and generate long-term economic value. Biomass-derived porous materials have emerged as a promising solution by transforming abundant agricultural, forestry, and industrial biomass residues into high-value hydrogen storage media, thereby supporting the principles of the circular bioeconomy and sustainable resource management. This study examines the economic value creation potential of biomass-based hydrogen storage technologies and evaluates their implications for green finance, low-carbon investment strategies, and sustainable development. It highlights how biomass valorization reduces dependence on expensive conventional storage materials while creating new revenue streams through waste utilization, carbon reduction, and renewable energy integration. The analysis demonstrates that biomass-derived activated carbons, biochars, carbon aerogels, and hierarchical porous carbon nanostructures possess favorable physicochemical properties, including high specific surface area, tunable pore architectures, lightweight characteristics, and excellent adsorption performance, making them attractive candidates for efficient hydrogen storage systems. From an economic perspective, these technologies improve supply-chain resilience, lower production costs, stimulate rural economic development, and encourage investment in sustainable manufacturing industries. Furthermore, biomass-based hydrogen storage aligns closely with green finance mechanisms by attracting environmental, social, and governance (ESG) investments, facilitating access to green bonds and climate finance, and supporting national decarbonization policies designed to achieve net-zero emissions. The study also discusses the integration of biomass-derived storage materials within emerging hydrogen value chains, emphasizing their contribution to carbon neutrality, renewable energy storage, and energy security. Despite challenges related to commercialization, feedstock standardization, scalability, and investment uncertainty, continued advances in material engineering, policy incentives, and financial innovation are expected to accelerate industrial deployment. Overall, biomass-derived hydrogen storage represents a strategically important intersection between sustainable materials science, green finance, and circular economic development, offering significant opportunities to create environmental, economic, and social value while supporting the global transition toward resilient and sustainable energy systems.

Based on the revised Sustainable Livelihoods Framework, fuzzy-set Qualitative Comparative Analysis was used to investigate the combinatorial effects of social, human, cultural, policy, physical, natural, and financial capital on herders' livelihood strategies in Chen Barag in the Hulunbuir Grassland, to identify the key drivers of herders’ adoption of a tourism livelihood. The results identify six differentiated livelihood configuration types. Traditional pastoral livelihoods are associated with resource-constrained configurations and high thresholds for tourism participation under ecological, market, and institutional conditions. Conversely, mixed livelihoods are associated with configurations involving productive resource release, locational advantages, cultural commodification, and intergenerational labour division, while governance tensions between institutional arrangements and diversified livelihood needs may create additional constraints. These findings highlight the joint importance of asset-related thresholds and institutional conditions in shaping differentiated tourism participation.

The practical value of non-Gaussian states for quantum computation, metrology, and networking is currently limited by the intrinsically low success rates of conventional generation methods like photon addition and subtraction. We introduce a scalable protocol that overcomes this bottleneck using postselected von Neumann measurements beyond the weak-coupling regime. Applied to standard Gaussian inputs, our method efficiently generates a suite of critical resources-including large-amplitude Schr & ouml;dinger cat states, Gottesman-Kitaev-Preskill-like states, and two-mode entangled states-with considerably higher success probabilities. Quantitative analysis of Wigner negativity and entanglement confirms their high quality. This work establishes postselected von Neumann measurement as a general and scalable principle for quantum resource generation, moving beyond a fundamental limitation in quantum state engineering.